Built for Enterprise Scale and Developer Productivity

Prediction as a capability your team consumes, not a model it maintains. Custom ML for the exceptional predictions. Aito for the ubiquitous ones.

The Database That Thinks

One store that searches and predicts over live data, with no training pipeline to build or maintain

Aito turns prediction into a query. Your team already consumes language models, embeddings and vector search as primitives rather than building them; Aito does the same for prediction over your own data. Each prediction is a query returning a calibrated probability and the evidence behind it, with no model artifact, no training pipeline and nothing to retrain when the data moves.

✓Query predictions like data: { predict: 'field', from: 'table', where: { field2: 'value' } }

✓No model training, deployment, or maintenance required

✓Single-digit millisecond queries: 6-8 ms on 100k rows, against Elasticsearch 8.15 at 4-5 ms

✓Real-time learning without pipeline complexity

Query-Time Intelligence: The Technical Innovation Behind Aito

Aito is a specialized database that performs statistical inference at query time, eliminating the need for pre-trained models

Lazy Learning Architecture

Unlike traditional ML that trains models upfront, Aito creates query-specific models on demand

Query-Time Model Creation

Each prediction query triggers real-time feature selection, concept learning, and Bayesian inference

Millisecond-scale model creation using specialized indexes for microsecond statistical operations

No Model Deployment

Models don't exist until queried - eliminating deployment, versioning, and drift issues

Stateless inference means no model artifacts to manage or maintain

Unified Statistical Engine

Same Bayesian foundation powers predictions, recommendations, and search

Text-book Bayesian approaches generalized across all query types

See It In Action

Here are 3 live queries that demonstrate Aito's predictive capabilities

Invoice Classification

Predict product category from invoice description

Aito Query

{
  "from": "invoices",
  "where": {
    "ProductName": "Cloud Services (AWS/GCP)",
    "TotalAmount": 5000,
    "InvoiceType": "Service"
  },
  "predict": "Processor",
  "select": [
    "$p",
    "Name",
    "Role"
  ]
}

Experience Aito's Full Capabilities

Explore complete applications built with Aito's predictive database. See personalized search, AI assistants, and document processing in action - all implemented in hours, not months.

Interactive Demo

Personalized Search

Same search query returns different results for Alice, Larry, and Veronica based on their preferences. See how AI personalizes "milk" searches for dietary restrictions.

• Real-time personalization• Context-aware ranking• Calibrated confidence on every result

Live AI

Conversational AI Assistants

Shopping assistants help customers find products while admin assistants provide business insights. Built with the same predictive database - no separate NLP infrastructure.

• Natural language queries• Business intelligence• Contextual recommendations

Enterprise Ready

Intelligent Document Processing

AI predicts GL codes, extracts payment terms, and detects anomalies from invoice data. The same system Posti uses to process 3,000+ invoices monthly with 95% accuracy.

• 95%+ accuracy rates• Production deployments• Real customer validation

Live demo → inspect logic → try with your data

Why Aito vs Traditional ML, LLMs, and Custom Development

Comprehensive comparison for technical decision-makers choosing AI approaches for structured data use cases

Traditional ML Infrastructure

Architecture:

Data Engineering

Feature Engineering

MLOps Platform

Key Limitations:

• 3-12 months timeline per use case

• Every use case carries its own lifecycle cost long after the model ships

• Every additional prediction is another training set, pipeline, monitor and owner

Large Language Models

Strengths:

Natural language understanding and generation

Complex reasoning and creative tasks

Conversational interfaces

Automation Challenge:

• Unreliable confidence metrics prevent automation

• Non-deterministic behavior breaks consistent workflows

While LLMs excel at reasoning and explanation, only predictive databases provide the reliable confidence metrics essential for business automation

Aito Predictive Database

Architecture:

Upload Data

Query Predictions

Integrate API Responses

Key Advantages:

• Hours to days implementation time

• Calibrated probabilities you can actually threshold: published ECE, not a softmax score

• Processes millions of records for complete context

Real-World Example: Invoice Processing Automation

GL Code prediction for "Cloud services for Bob Johnson's IT infrastructure project"

Traditional ML

Build NLP pipeline → Extract entities → Train classification model → Deploy MLOps infrastructure

Complex system requiring ML expertise and ongoing maintenance

LLM Approach

Send invoice text to LLM with few-shot examples

Inconsistent confidence scores prevent automation - requires human review for every invoice

Aito Approach

Upload invoice data → Query for prediction with confidence score

Returns a calibrated probability you can threshold, the evidence behind it, and an automatic Bob→employee match. Act above the bar, ask a person below it

The Strategic Hybrid Approach

Most enterprises benefit from combining approaches strategically

Rapid deployment for 80% of enterprise AI use cases

Reliable automation with intelligent escalation

Cost-effective scaling without MLOps complexity

Measured, and published with the losses

Every figure below links to the benchmark that produced it, including the rows where Aito loses.

0 s

Training time

At 1M rows: Random Forest 775 s, LightGBM 2,332 s, Aito 0 s. There is no training step.

2.3x

Smaller on disk than v1

3,258 MB against 7,617 MB at 10M rows, one machine, one scale.

6-8 ms

Warm query latency

Filter and full-text on 100k rows, against Elasticsearch 8.15 at 4-5 ms. Predictions at 10M rows are slower than v1 on link targets.

Performance Benchmarks & Production Validation

Our own published benchmarks, run on a single machine and linked to their measurement, plus two customer deployments.

Key Performance Metrics

Query latency on 100k invoice rows6-8 msElasticsearch 8.15 runs the same queries in 4-5 ms. It is faster on every one, by roughly 1.2 to 1.7 times, and the gap is milliseconds nobody notices

Search relevance, nDCG@10, against BM25 at 0.4680.545Generated 600-product corpus, 300 held-out searches, one seed. Real query logs are messier

Prediction latency at 10M rows, by target160-396 msglCode 160 ms, acceptor 355 ms, processor 396 ms. v2 is slower than v1 on two of the three; we publish the row

Invoice acceptor, against a tuned AutoML pipeline at 56.2%61.3%2,000-row hold-out, 95% interval 59.1-63.4%. No training step; the AutoML search trained for 418 s

Production accuracy (Nordic enterprise AP automation)95%+thousands of invoices/month on SAP + UiPath stacks since 2018

Implementation timeHoursvs 3-12 months for traditional ML

Production Validation

Real customer deployments in production environments

Nordic enterprise logisticsthousands of invoices/month, since 201895%+ accuracy requirement consistently met

GridPane (Technology)Cloud infrastructure2-hour implementation time

Why Engineering Teams Choose Aito

Eliminate MLOps Complexity

No pipelines, no model deployments, no infrastructure to maintain. Just query predictions like data.

Replace traditional ML infrastructure with simple HTTP requests for prediction and classification use cases

Multi-Purpose Intelligence

One system handles predictions, recommendations, search, and analytics for structured data scenarios.

Single database serves structured data AI use cases through unified query interface

Developer Productivity

Existing development team can implement AI features. No specialized ML expertise required.

SQL-like syntax with comprehensive API documentation and SDK support

Built for Enterprise Requirements

Flexible deployment options and data governance for regulated environments

Deployment Flexibility

Deploy in your own cloud infrastructure or use EU-hosted managed service

Available on AWS, Azure, GCP in customer accounts or managed EU (Ireland) hosting

Data Sovereignty

Complete control over data location and access with no vendor lock-in

Customer data never leaves your designated environment with full backup/export capabilities

GDPR Compliance

Built-in compliance features for European data protection requirements

Data processing agreements, right to deletion, consent management, and audit trails

Total Cost of Ownership Analysis

The cost that matters is not the team. It is the lifecycle each prediction carries after it ships, and whether an ordinary prediction can justify one.

Traditional ML

A training set per prediction

A pipeline per prediction

Monitoring and drift detection per prediction

A named owner per prediction

Scales with the number of predictions

A project per model

Aito Approach

One store, one integration

No model artifacts to version or deploy

Nothing to retrain when the data moves

Scales with usage, not with the number of predictions

A query per prediction

Cost Savings Breakdown

Adding the first predictionA projectA query

Adding the sixteenthAnother project, and fifteen still to maintainAnother query. ecommerce.aito.ai runs sixteen predictive views on one dataset with no per-view models

When the data movesDetect drift, retrain, revalidate, redeployThe next query already reflects it

Add the predictive half this afternoon.

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Built for Enterprise Scale and Developer Productivity

The Database That Thinks

One store that searches and predicts over live data, with no training pipeline to build or maintain

Query-Time Intelligence: The Technical Innovation Behind Aito

Lazy Learning Architecture

Query-Time Model Creation

No Model Deployment

Unified Statistical Engine

See It In Action

Invoice Classification

Experience Aito's Full Capabilities

Personalized Search

Conversational AI Assistants

Intelligent Document Processing

Why Aito vs Traditional ML, LLMs, and Custom Development

Traditional ML Infrastructure

Architecture:

Large Language Models

Strengths:

Aito Predictive Database

Architecture:

Real-World Example: Invoice Processing Automation

Traditional ML

LLM Approach

Aito Approach

The Strategic Hybrid Approach

Measured, and published with the losses

Performance Benchmarks & Production Validation

Key Performance Metrics

Production Validation

Why Engineering Teams Choose Aito

Eliminate MLOps Complexity

Multi-Purpose Intelligence

Developer Productivity

Built for Enterprise Requirements

Deployment Flexibility

Data Sovereignty

GDPR Compliance

Total Cost of Ownership Analysis

Traditional ML

Aito Approach

Cost Savings Breakdown

Technical Resources for Architects

Demo Workbook

The Science Behind Aito

Predictive Queries vs ML Models - Technical Analysis

API Documentation

Getting Started Guide

Add the predictive half this afternoon.

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